0%

Book Description

This concise, easy-to-use reference puts one of the most popular frameworks for deep learning research and development at your fingertips. Author Joe Papa provides instant access to syntax, design patterns, and code examples to accelerate your development and reduce the time you spend searching for answers.

Research scientists, machine learning engineers, and software developers will find clear, structured PyTorch code that covers every step of neural network developmentā€”from loading data to customizing training loops to model optimization and GPU/TPU acceleration. Quickly learn how to deploy your code to production using AWS, GCP, or Azure, and your ML models to mobile and edge devices.

  • Learn basic PyTorch syntax and design patterns
  • Create custom models and data transforms
  • Train and deploy models using a GPU and TPU
  • Train and test a deep learning classifier
  • Accelerate training using optimization and distributed training
  • Access useful PyTorch libraries and the PyTorch ecosystem

Table of Contents

  1. 1. An Introduction to PyTorch
    1. What is PyTorch?
    2. Why use PyTorch?
    3. Getting Started
      1. Running in Google Colaboratory
      2. Running on Local Computer
      3. Running on Cloud Platforms
      4. Verifying Your PyTorch Environment
  2. 2. Tensors
    1. What is a Tensor?
      1. Simple CPU Example
      2. Simple GPU Example
      3. Moving Tensors between CPU & GPU
    2. Creating Tensors
      1. Tensor Attributes
      2. Data Types
      3. Creating Tensors from Random Samples
      4. Creating Tensors Like Other Tensors
    3. Tensor Operations
      1. Indexing, Slicing, Combining & Splitting Tensors
      2. Tensor Operations for Mathematics
      3. Automatic Differentiation (Autograd)
54.81.33.119